Biology subjects

Santamaria, P. G.

Publications and source records attributed to Santamaria, P. G..

3 recordsLinked to original sources

bioRxiv2026

WNT-driven chromosomal instability as a biomarker for PORCN inhibition

Targeting Porcupine (PORCN), a key regulator of the WNT-signalling pathway, has shown therapeutic potential in multiple cancers. Despite strong target engagement and acceptable safety profiles through human phase I clinical trials, low phase II efficacy has stalled further clinical development. Given that aberrant WNT signalling can drive tumorigenesis by inducing chromosomal instability (CIN), we hypothesised that genomic CIN signatures might serve as a predictive biomarker to help improve response rates. Using a controlled in vitro model and single-cell whole-genome sequencing, we demonstrate that acute WNT-activation directly induces three distinct types of CIN: whole genome duplication, replication stress, and impaired homologous recombination. We translated these observations into a composite CIN signature biomarker that significantly correlated with both genetic dependency and pharmacological inhibition of PORCN across 195 and 24 cell lines, respectively. Through a large-scale meta-analysis of patient-derived and cell line xenografts, we established that this composite CIN signature biomarker quantitatively predicts in vivo PORCN inhibitor sensitivity (R=-0.71, p<0.002). By applying an optimised biomarker threshold, refined through modelling of human patient data, to the The Cancer Genome Atlas dataset, we successfully retrospectively modelled previous trial results and identified gastroesophageal cancers as a high-prevalence (36.6%) indication for future development. We validated this strategy in a mouse clinical trial of gastric and esophageal xenografts, where biomarker-guided stratification achieved an objective response rate of 60% and significantly decreased risk of progression (HR=0.21, p=0.0345). These data establish an actionable, trail-ready framework for further PORCN inhibitor clinical development.

cancer biology↗

bioRxiv2025

Tracking ongoing chromosomal instability using single-cell whole-genome sequencing

Chromosomal instability (CIN) generates aneuploid genomes that are characteristic of most cancers. While bulk genome sequencing reveals historical CIN, it lacks the resolution to identify ongoing CIN that actively shapes genome evolution. Here, we present a computational framework that leverages single-cell whole-genome sequencing (scWGS) to identify and quantify ongoing CIN by detecting cell-unique copy number alterations and probabilistically mapping them to known CIN signatures. We validated this framework generating in vitro models with four types of induced CIN, correctly identifying the induced-CIN type in each case. When applied to cell lines and organoids with ongoing homologous recombination deficiency, our method showed improved identification of sensitivity to PARP inhibition and platinum-based chemotherapy. Analysing scWGS data from 8 triple-negative breast cancers, we linked ongoing impaired non-homologous end joining to subclonal diversification, a finding further supported in cohorts of 179 unmatched primary and metastatic TNBCs and 39 matched cases. Collectively, our results demonstrate that distinguishing ongoing from historical chromosomal instability uncovers a distinct dimension of tumour evolution, suggesting that effective precision oncology will require integrating measurements of both past genomic scars and active mutational processes.

genomics↗

bioRxiv2025

Forecasting oncogene amplification and tumour suppressor deletion

Oncogene amplification and tumour suppressor deletion can drive tumour initiation, progression and treatment resistance. Detection at diagnosis often signals poor prognosis, but it can also enable opportunities for treatment with highly effective targeted therapies. Predicting the likelihood that a patient will acquire these driver alterations in the future using a genomic test represents an opportunity to realise the benefits of interventions earlier, potentially with preventative intent. Here, we present a forecasting framework that takes as input a DNA copy number profile and predicts whether the tumour will acquire an oncogene amplification or tumour suppressor deletion in the future. This framework leverages mutation rate estimates from the input tumour, alongside gene-specific selection coefficients derived from a large cohort of 7,880 tumours. We demonstrate feasibility using 7,042 single-time-point samples and longitudinally collected tumour pairs from 44 prostate and 100 lung cancers, identifying tumours that went on to acquire amplifications at a later time point with an average AUC of 0.87. We show potential clinical utility by forecasting poor prognosis in low-grade gliomas via CDK4/PDGFRA amplification or CDKN2A deletion, and osimertinib resistance in lung cancers via MET amplification. This study serves as a proof-of-concept for a new class of biomarker, wherein selective pressures and mutation-generating processes can be harnessed to anticipate future genomic alterations.

genomics↗